You create an Azure Machine Learning workspace. You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics. You need…

The correct answer is D. mlflow.log_text(). 'mlflow.log_text(text, artifact_file)' logs a string value as a text artifact, making it the correct choice for recording string data. 'mlflow.log_metric()' only accepts numeric (float) values and will raise an error if passed a string. 'mlflow.log_artifact()' uploads a local…

Explore data and run experiments

Question

You create an Azure Machine Learning workspace. You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics. You need to implement the method to log the string metrics. Which method should you use?

Options

Amlflow.log_artifact()
Bmlflow.log.dict()
Cmlflow.log_metric()
Dmlflow.log_text()

How the community answered

(16 responses)

A
6% (1)
C
6% (1)
D
88% (14)

Explanation

'mlflow.log_text(text, artifact_file)' logs a string value as a text artifact, making it the correct choice for recording string data. 'mlflow.log_metric()' only accepts numeric (float) values and will raise an error if passed a string. 'mlflow.log_artifact()' uploads a local file path, not an in-memory string. 'mlflow.log.dict()' uses incorrect dot notation - the valid method is 'mlflow.log_dict()', which logs a dictionary as a JSON/YAML artifact, not a plain string.

Topics

#Azure Machine Learning#MLflow#Experiment Tracking#Logging Metrics

Community Discussion

No community discussion yet for this question.

Full DP-100 Practice